MiRuntime.com
Memory and Evidence
A Machine Intelligence Runtime needs typed memory and durable evidence trails so AI work is repeatable, reviewable, and trustworthy.
Memory is not a pile of old context. Evidence is not a generic log. In a Machine Intelligence Runtime, both must be structured enough to support user trust, debugging, compliance, and future work.
Memory
Memory gives the runtime continuity. It can store user preferences, project facts, prior decisions, reusable instructions, and task state. It must also define what should not be remembered.
Evidence
Evidence gives the runtime accountability. It records the task path, files used, tools called, changes made, approvals granted, and uncertainty left unresolved.
Memory should be typed
Short-term task memory
Temporary context for the current task. It should expire or be summarized when the work is complete.
Project memory
Persistent decisions, domain rules, architecture notes, brand constraints, and project-specific vocabulary.
User memory
Stable preferences and instructions that improve future work without over-collecting personal detail.
Prohibited memory
Explicit exclusions for secrets, sensitive facts, temporary assumptions, and content that should never be reused.
Evidence should be exportable
When an intelligent workflow modifies code, drafts a message, summarizes records, generates a report, or makes a recommendation, the user should be able to inspect the run. Evidence should make the work reproducible enough for review.
- Original request and interpreted objective
- Context and memory applied to the task
- Tool calls, inputs, outputs, errors, and retries
- Files read, created, modified, or deleted
- Approvals requested and granted
- Final artifacts and unresolved uncertainty
The future runtime leaves a trail.
That trail is not noise. It is how users keep control over increasingly capable systems.